"""simple docstring"""



import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				int(_A)
     __snake_case		,						__snake_case		,						__snake_case				=				t // 36_00, (t // 60) % 60, t % 60
     return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}"





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case=3_00):
     return f"\n    <div>\n      {prefix}\n      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n      {label}\n    </div>\n    "





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				'''<table border=\"1\" class=\"dataframe\">\n'''
     html_code += """  <thead>\n <tr style="text-align: left;">\n"""
     for i in items[0]:
          html_code += f"      <th>{i}</th>\n"
     html_code += "    </tr>\n  </thead>\n  <tbody>\n"
     for line in items[1:]:
          html_code += "    <tr>\n"
          for elt in line:
               __snake_case				=				f"{elt:.6f}" if isinstance(_A,   _A) else str(_A)
               html_code += f"      <td>{elt}</td>\n"
          html_code += "    </tr>\n"
     html_code += "  </tbody>\n</table><p>"
     return html_code



class   _A						:




    """simple docstring"""




    UpperCamelCase_						:       List[str]      					=       5
    UpperCamelCase_						:       Optional[int]      					=       0.2
    def __init__(		self				:  List[Any]						,   A_				:  int						,   A_				:  Optional[str] = None						,   A_				:  bool = True						,   A_				:  Optional["NotebookTrainingTracker"] = None						,   A_				:  int = 300						,   )     ->							Dict:
         __snake_case				=				total
         __snake_case				=				'''''' if prefix is None else prefix
         __snake_case				=				leave
         __snake_case				=				parent
         __snake_case				=				width
         __snake_case				=				None
         __snake_case				=				None
         __snake_case				=				None
    def 			lowercase       (		self				:  List[Any]						,   A_				:  int						,   A_				:  bool = False						,   A_				:  str = None     )     ->							int:
         __snake_case				=				value
         if comment is not None:
              __snake_case				=				comment
         if self.last_value is None:
              __snake_case				=				__snake_case				=				time.time()
              __snake_case				=				__snake_case				=				value
              __snake_case				=				__snake_case				=				None
              __snake_case				=				self.warmup
              __snake_case				=				1
              self.update_bar(__UpperCamelCase     )
         elif value <= self.last_value and not force_update:
              return
         elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for						,   self.total     ):
              if self.first_calls > 0:
                   self.first_calls -= 1
              __snake_case				=				time.time()
              __snake_case				=				current_time - self.start_time
              # We could have value = self.start_value if the update is called twixe with the same start value.
              if value > self.start_value:
                   __snake_case				=				self.elapsed_time / (value - self.start_value)
              else:
                   __snake_case				=				None
              if value >= self.total:
                   __snake_case				=				self.total
                   __snake_case				=				None
                   if not self.leave:
                        self.close()
              elif self.average_time_per_item is not None:
                   __snake_case				=				self.average_time_per_item * (self.total - value)
              self.update_bar(__UpperCamelCase     )
              __snake_case				=				value
              __snake_case				=				current_time
              if self.average_time_per_item is None:
                   __snake_case				=				1
              else:
                   __snake_case				=				max(int(self.update_every / self.average_time_per_item     )						,   1     )
    def 			lowercase       (		self				:  str						,   A_				:  int						,   A_				:  Union[str, Any]=None     )     ->							str:
         __snake_case				=				''' ''' * (len(str(self.total     )     ) - len(str(__UpperCamelCase     )     )) + str(__UpperCamelCase     )
         if self.elapsed_time is None:
              __snake_case				=				f"[{spaced_value}/{self.total} : < :"
         elif self.predicted_remaining is None:
              __snake_case				=				f"[{spaced_value}/{self.total} {format_time(self.elapsed_time     )}"
         else:
              __snake_case				=				(
                  f"[{spaced_value}/{self.total} {format_time(self.elapsed_time     )} <"
                  f" {format_time(self.predicted_remaining     )}"
              )
              self.label += f", {1/self.average_time_per_item:.2f} it/s"
         self.label += "]" if self.comment is None or len(self.comment     ) == 0 else f", {self.comment}]"
         self.display()
    def 			lowercase       (		self				:  List[str]     )     ->							List[Any]:
         __snake_case				=				html_progress_bar(self.value						,   self.total						,   self.prefix						,   self.label						,   self.width     )
         if self.parent is not None:
              # If this is a child bar, the parent will take care of the display.
              self.parent.display()
              return
         if self.output is None:
              __snake_case				=				disp.display(disp.HTML(self.html_code     )						,   display_id=__UpperCamelCase     )
         else:
              self.output.update(disp.HTML(self.html_code     )     )







    def 			lowercase       (		self				:  str     )     ->							int:






         if self.parent is None and self.output is not None:
              self.output.update(disp.HTML(''''''     )     )






class   _A						(    _UpperCAmelCase       ):




    """simple docstring"""




    def __init__(		self				:  Dict						,   A_				:  List[Any]						,   A_				:  str=None     )     ->							str:
         super().__init__(__UpperCamelCase     )
         __snake_case				=				None if column_names is None else [column_names]
         __snake_case				=				None
    def 			lowercase       (		self				:  Optional[Any]     )     ->							Optional[int]:
         __snake_case				=				html_progress_bar(self.value						,   self.total						,   self.prefix						,   self.label						,   self.width     )
         if self.inner_table is not None:
              self.html_code += text_to_html_table(self.inner_table     )
         if self.child_bar is not None:
              self.html_code += self.child_bar.html_code
         if self.output is None:
              __snake_case				=				disp.display(disp.HTML(self.html_code     )						,   display_id=__UpperCamelCase     )
         else:
              self.output.update(disp.HTML(self.html_code     )     )
    def 			lowercase       (		self				:  Optional[Any]						,   A_				:  List[Any]     )     ->							Union[str, Any]:






         if self.inner_table is None:
              __snake_case				=				[list(values.keys()     ), list(values.values()     )]
         else:
              __snake_case				=				self.inner_table[0]
              if len(self.inner_table     ) == 1:
                   # We give a chance to update the column names at the first iteration
                   for key in values.keys():
                        if key not in columns:
                             columns.append(__UpperCamelCase     )
                   __snake_case				=				columns
              self.inner_table.append([values[c] for c in columns]     )
    def 			lowercase       (		self				:  Dict						,   A_				:  Union[str, Any]						,   A_				:  Union[str, Any]=None						,   A_				:  List[str]=300     )     ->							List[Any]:
         __snake_case				=				NotebookProgressBar(__UpperCamelCase						,   prefix=__UpperCamelCase						,   parent=self						,   width=__UpperCamelCase     )
         return self.child_bar







    def 			lowercase       (		self				:  int     )     ->							int:
         __snake_case				=				None
         self.display()






class   _A						(    _UpperCAmelCase       ):




    """simple docstring"""




    def __init__(		self				:  Any     )     ->							List[str]:
         __snake_case				=				None
         __snake_case				=				None
         __snake_case				=				False
    def 			lowercase       (		self				:  str						,   A_				:  List[str]						,   A_				:  Optional[int]						,   A_				:  Dict						,   **A_				:  Dict     )     ->							str:
         __snake_case				=				'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
         __snake_case				=				0
         __snake_case				=				0
         __snake_case				=				[self.first_column] + ['''Training Loss''']
         if args.evaluation_strategy != IntervalStrategy.NO:
              column_names.append('''Validation Loss'''     )
         __snake_case				=				NotebookTrainingTracker(state.max_steps						,   __UpperCamelCase     )
    def 			lowercase       (		self				:  Optional[Any]						,   A_				:  Union[str, Any]						,   A_				:  List[str]						,   A_				:  List[Any]						,   **A_				:  Dict     )     ->							Optional[int]:
         __snake_case				=				int(state.epoch     ) if int(state.epoch     ) == state.epoch else f"{state.epoch:.2f}"
         self.training_tracker.update(
             state.global_step + 1						,   comment=f"Epoch {epoch}/{state.num_train_epochs}"						,   force_update=self._force_next_update						,   )
         __snake_case				=				False
    def 			lowercase       (		self				:  Optional[int]						,   A_				:  Optional[Any]						,   A_				:  Dict						,   A_				:  Any						,   A_				:  Any=None						,   **A_				:  List[Any]     )     ->							List[Any]:






         if not has_length(__UpperCamelCase     ):
              return
         if self.prediction_bar is None:
              if self.training_tracker is not None:
                   __snake_case				=				self.training_tracker.add_child(len(__UpperCamelCase     )     )
              else:
                   __snake_case				=				NotebookProgressBar(len(__UpperCamelCase     )     )
              self.prediction_bar.update(1     )
         else:
              self.prediction_bar.update(self.prediction_bar.value + 1     )
    def 			lowercase       (		self				:  Dict						,   A_				:  str						,   A_				:  str						,   A_				:  str						,   **A_				:  Tuple     )     ->							Optional[int]:






         if self.prediction_bar is not None:
              self.prediction_bar.close()
         __snake_case				=				None
    def 			lowercase       (		self				:  List[str]						,   A_				:  List[Any]						,   A_				:  Optional[int]						,   A_				:  Any						,   A_				:  Any=None						,   **A_				:  Dict     )     ->							Dict:






         if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
              __snake_case				=				{'''Training Loss''': logs['''loss''']}
              # First column is necessarily Step sine we're not in epoch eval strategy
              __snake_case				=				state.global_step
              self.training_tracker.write_line(__UpperCamelCase     )
    def 			lowercase       (		self				:  Optional[Any]						,   A_				:  Tuple						,   A_				:  Any						,   A_				:  Dict						,   A_				:  List[Any]=None						,   **A_				:  Tuple     )     ->							str:






         if self.training_tracker is not None:
              __snake_case				=				{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
              for log in reversed(state.log_history     ):
                   if "loss" in log:
                        __snake_case				=				log['''loss''']
                        break

              if self.first_column == "Epoch":
                   __snake_case				=				int(state.epoch     )
              else:
                   __snake_case				=				state.global_step
              __snake_case				=				'''eval'''
              for k in metrics:
                   if k.endswith('''_loss'''     ):
                        __snake_case				=				re.sub(R'''\_loss$'''						,   ''''''						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop('''total_flos'''						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop('''epoch'''						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop(f"{metric_key_prefix}_runtime"						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop(f"{metric_key_prefix}_samples_per_second"						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop(f"{metric_key_prefix}_steps_per_second"						,   __UpperCamelCase     )
              __snake_case				=				metrics.pop(f"{metric_key_prefix}_jit_compilation_time"						,   __UpperCamelCase     )
              for k, v in metrics.items():
                   if k == f"{metric_key_prefix}_loss":
                        __snake_case				=				v
                   else:
                        __snake_case				=				k.split('''_'''     )
                        __snake_case				=				''' '''.join([part.capitalize() for part in splits[1:]]     )
                        __snake_case				=				v
              self.training_tracker.write_line(__UpperCamelCase     )
              self.training_tracker.remove_child()
              __snake_case				=				None
              # Evaluation takes a long time so we should force the next update.
              __snake_case				=				True







    def 			lowercase       (		self				:  List[Any]						,   A_				:  Dict						,   A_				:  Optional[int]						,   A_				:  int						,   **A_				:  Union[str, Any]     )     ->							Any:
         self.training_tracker.update(
             state.global_step						,   comment=f"Epoch {int(state.epoch     )}/{state.num_train_epochs}"						,   force_update=__UpperCamelCase     )
         __snake_case				=				None

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()